The Loan Prediction Project is a machine learning-based solution designed to predict the likelihood of a loan application being approved. The project demonstrates an end-to-end data science workflow, including data preprocessing, feature selection, model training, and evaluation. This solution can assist financial institutions in making informed decisions regarding loan approvals.
- Data Preprocessing: Cleans and prepares raw data for analysis.
- Exploratory Data Analysis (EDA): Analyzes key patterns and trends in the dataset.
- Feature Engineering: Selects and transforms significant features to enhance model performance.
- Model Training: Implements various machine learning algorithms to predict loan status.
- Model Evaluation: Assesses model accuracy and reliability using evaluation metrics.
- Deployment: Interactive app built using Streamlit for real-time loan predictions.
- Programming Language: Python
- Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Streamlit
- IDE: Jupyter Notebook
To set up and run the project locally, follow these steps:
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Clone the repository:
git clone <repository_url>
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Navigate to the project directory:
cd loan-prediction-project -
Create a virtual environment (optional):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install the required dependencies:
pip install -r requirements.txt
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Run the Streamlit app:
streamlit run app.py
- Load the dataset.
- Perform data preprocessing and feature selection.
- Train machine learning models.
- Evaluate the performance of different models.
- Use the Streamlit app for predictions by providing input parameters.
loan-prediction-project/
├── data/ # Dataset files
├── notebooks/ # Jupyter Notebook for analysis
├── scripts/ # Python scripts for preprocessing and modeling
├── app.py # Streamlit app for deployment
├── requirements.txt # Project dependencies
├── README.md # Project documentation
The dataset used in this project includes various features such as:
- Applicant Income
- Loan Amount
- Credit History
- Property Area
- Loan Status (Target variable)
The project achieved a high level of accuracy using machine learning models such as Logistic Regression, Random Forest, and Gradient Boosting. Detailed evaluation metrics are included in the notebook.
- Enhancing the model by incorporating additional features.
- Implementing advanced algorithms for better performance.
- Expanding the application to handle real-time data inputs.
This project is licensed under the MIT License.
Special thanks to the open-source community and datasets used for this project.
Feel free to contribute to this project by submitting issues or pull requests!